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Intention Estimation Using Set of Reference Trajectories as Behaviour Model
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
2018 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 12, article id 4423Article in journal (Refereed) Published
Abstract [en]

Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method. © 2018 by the authors

Place, publisher, year, edition, pages
Basel: MDPI, 2018. Vol. 18, no 12, article id 4423
Keywords [en]
behaviour modelling, intention estimation
National Category
Robotics Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-38614DOI: 10.3390/s18124423Scopus ID: 2-s2.0-85058645512OAI: oai:DiVA.org:hh-38614DiVA, id: diva2:1270883
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CAISR/SAS2
Funder
Knowledge FoundationAvailable from: 2018-12-14 Created: 2018-12-14 Last updated: 2019-01-02Bibliographically approved

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Muhammad, NaveedÅstrand, Björn

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